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PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase.

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Title: PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase.
Authors: Németh, Flóra B., Hamza, Andrea, Tugyi, Beatrix, El-Ali, Maya, Szegletes, Luca, Madarász, Ádám, Pápai, Imre
Source: NPJ Computational Materials; 12/7/2025, Vol. 11, p1-10, 10p
Subject Terms: REDUCTION potential, DEEP learning, AQUEOUS solutions, ELECTROACTIVE substances, ELECTROCHEMICAL apparatus, ORGANIC compounds, SINGLE electron transfer mechanisms
Abstract: An interactive web tool, PredPotS, has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions. The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds. The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method (GFN2-xTB) and a well-established DFT approach (M06-2X functional along with the SMD solvent model). While this computational approach is cost-effective, it is subject to certain limitations, which are nonetheless duly accounted for in the development of the database. The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics. By entering or uploading the SMILES codes of the molecules, PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes. The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries, but it may also prove useful in a variety of other electrochemical applications. [ABSTRACT FROM AUTHOR]
Copyright of NPJ Computational Materials is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: PredPotS: web tool for predicting one-electron standard reduction potentials for organic molecules in aqueous phase.
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  Data: NPJ Computational Materials; 12/7/2025, Vol. 11, p1-10, 10p
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  Data: An interactive web tool, PredPotS, has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions. The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds. The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method (GFN2-xTB) and a well-established DFT approach (M06-2X functional along with the SMD solvent model). While this computational approach is cost-effective, it is subject to certain limitations, which are nonetheless duly accounted for in the development of the database. The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics. By entering or uploading the SMILES codes of the molecules, PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes. The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries, but it may also prove useful in a variety of other electrochemical applications. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of NPJ Computational Materials is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1038/s41524-025-01890-1
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              Text: 12/7/2025
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